Using Torrent Inflation to Efficiently Serve the Long Tail in Peer-Assisted Content Delivery Systems

Abstract

Abstract.A peer-assisted content delivery system uses the upload bandwidth of its clients to assist in delivery of popular content. In peerassisted systems using a BitTorrent-like protocol, a content delivery server seeds the offered files, and active torrents form when multiple clients make closely-spaced requests for the same content. Scalability is achieved in the sense of being able to accommodate arbitrarily high request rates for individual files. Scalability with respect to the number of files, however, may be much more difficult to achieve, owing to a "long tail" of lukewarm or cold files for which the server may need to assume most or all of the delivery cost. This paper first addresses the question of how best to allocate server resources among multiple active torrents. We then propose new content delivery policies that use some of the available upload bandwidth from currently downloading clients to "inflate" torrents for files that would otherwise require substantial server bandwidth. Our performance results show that use of torrent inflation can substantially reduce download times, by more than 50% in some cases.

Dynamic file bundling for large-scale content distribution

“…As in our steady-state experiments, we use two scenarios: one with purely random file requests, in which the popular file is requested with higher probability than the less popular ones, and one in which the files requested by each node are statically assigned. The results for both cases are similar, and for the purpose of evaluation, only results for (1) actual (2) max (3)(4)(5)(6)(7)(8)(9)(10) mean (3)(4)(5)(6)(7)(8)(9)(10) min (3)(4)(5)(6)(7)(8)(9)(10) Fig. 7: Number of active downloaders of each file type, using the random bundling policy for the dynamic experiments.…”

“…One of the most promising solutions is to request that peers help in the download of other files [7], [8], [16], [19]. To the best of our knowledge, the work by Menasche et al [8], who apply a queuing model and static bundling experiments to illustrate the problem, and a simulation-based evaluation of dynamic bundling policies by Carlsson et al [7] are closest to our current work. We are not aware of any works that design, implement, and evaluate a prototype system.…”

“…By splitting each file into many small pieces, each piece can be downloaded from different peers and/or servers. The approach allows flexible distribution paths, based on current piece availability, and is highly effective for popular content, for which the request rates enable the formation of self-sustaining torrents, where the entire content of the file is available among the peers themselves [6], [7]. On the other hand, the approach does not efficiently serve the contents in the long tail, for which the request rates are not sufficient for the corresponding torrents to be self-sustaining, and the contents must instead be served primarily using server (or seed) bandwidth.…”

“…Both static [8], [9] and dynamic [7] bundling approaches have been proposed. With static bundling, a pre-determined file collection is grouped together by the publisher.…”

Tradeoffs in cloud and peer-assisted content delivery systems

“…During this time period, the peers can assist the system in three ways: (i) upload pieces of the content they are currently downloading, (ii) seeding content that they have downloaded in the past, and (iii) through dynamic bundling. Both peer seeding and dynamic bundling (or torrent inflation) [8] effectively improve the availability, or in our case reduce the server bandwidth requirements. In contrast to seeding, dynamic bundling (or inflation) has the advantage that no persistent storage is required on the peers.…”